A Novel Role for NUAK1 in Promoting Ovarian Cancer Metastasis through Regulation of Fibronectin Production in Spheroids
Bibliographic record
Abstract
Epithelial ovarian cancer (EOC) has a unique mode of metastasis, where cells shed from the primary tumour, form aggregates called spheroids to evade anoikis, spread through the peritoneal cavity, and adhere to secondary sites. We previously showed that the master kinase Liver kinase B1 (LKB1) is required for EOC spheroid viability and metastasis. We have identified novel (nua) kinase 1 (NUAK1) as a top candidate LKB1 substrate in EOC cells and spheroids using a multiplex inhibitor beads-mass spectrometry approach. We confirmed that LKB1 maintains NUAK1 phosphorylation and promotes its stabilization. We next investigated NUAK1 function in EOC cells. Ectopic NUAK1-overexpressing EOC cell lines had increased adhesion, whereas the reverse was seen in OVCAR8-NUAK1KO cells. In fact, cells with NUAK1 loss generate spheroids with reduced integrity, leading to increased cell death after long-term culture. Following transcriptome analysis, we identified reduced enrichment for cell interaction gene expression pathways in OVCAR8-NUAK1KO spheroids. In fact, the FN1 gene, encoding fibronectin, exhibited a 745-fold decreased expression in NUAK1KO spheroids. Fibronectin expression was induced during native spheroid formation, yet this was completely lost in NUAK1KO spheroids. Co-incubation with soluble fibronectin restored the compact spheroid phenotype to OVCAR8-NUAK1KO cells. In a xenograft model of intraperitoneal metastasis, NUAK1 loss extended survival and reduced fibronectin expression in tumours. Thus, we have identified a new mechanism controlling EOC metastasis, through which LKB1-NUAK1 activity promotes spheroid formation and secondary tumours via fibronectin production.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".